Quantum-Behaved Particle Swarm Optimization Algorithm with Adaptive Mutation Based on q-Gaussian Distribution

Aiming at the drawback of being easily trapped into the local optima and premature convergence in quantum-behaved particle swarm optimization algorithm, quantum-behaved particle swarm optimization algorithm with adaptive mutation based on q-Gaussian distribution is proposed. q-Gaussian mutation operator is applied to the mean best position of particles to overcome the drawback of premature convergence caused by loss of diversity in the population. In the evolution of population, adaptive adjustment of the nonextensive entropic index q balances exploration and exploitation. The simulation results of testing four standard benchmark functions and traveling salesman problem show that quantum-behaved particle swarm optimization algorithm with adaptive mutation based on q-Gaussian distribution has best optimization performance and robustness.